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A high precision feature based on LBP and Gabor theory for face recognition.

Xia W, Yin S, Ouyang P - Sensors (Basel) (2013)

Bottom Line: A maximum improvement of 29.41% is achieved comparing with other methods.Besides, the ROC curve provides a satisfactory figure.Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

View Article: PubMed Central - PubMed

Affiliation: Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China. maxiaola@gmail.com

ABSTRACT
How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

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Related in: MedlinePlus

The finally new feature. Each column (P0∼P7) represents the sub-feature at different orientations of the center point. Each row (L1∼L4) represents the final BG2D2LRP feature of the center point.
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f5-sensors-13-04499: The finally new feature. Each column (P0∼P7) represents the sub-feature at different orientations of the center point. Each row (L1∼L4) represents the final BG2D2LRP feature of the center point.

Mentions: Here we list the encoded features in turn just like the way shown in Figure 5. The detailed result in Figure 5 is an example based on the model in Figure 3. For computational convenience, we transfer the eight binary codes at each layer into a decimal number. These four decimal numbers contain almost all the required information for a square.


A high precision feature based on LBP and Gabor theory for face recognition.

Xia W, Yin S, Ouyang P - Sensors (Basel) (2013)

The finally new feature. Each column (P0∼P7) represents the sub-feature at different orientations of the center point. Each row (L1∼L4) represents the final BG2D2LRP feature of the center point.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3673096&req=5

f5-sensors-13-04499: The finally new feature. Each column (P0∼P7) represents the sub-feature at different orientations of the center point. Each row (L1∼L4) represents the final BG2D2LRP feature of the center point.
Mentions: Here we list the encoded features in turn just like the way shown in Figure 5. The detailed result in Figure 5 is an example based on the model in Figure 3. For computational convenience, we transfer the eight binary codes at each layer into a decimal number. These four decimal numbers contain almost all the required information for a square.

Bottom Line: A maximum improvement of 29.41% is achieved comparing with other methods.Besides, the ROC curve provides a satisfactory figure.Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

View Article: PubMed Central - PubMed

Affiliation: Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China. maxiaola@gmail.com

ABSTRACT
How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.

Show MeSH
Related in: MedlinePlus